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Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions
Delft Univ Technol, Interact Intelligence, Dept Intelligent Syst, Delft, Netherlands..
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-3687-6189
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-8874-6629
Eyeware Tech SA, Martigny, Switzerland..
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2021 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 8, article id 555913Article in journal (Refereed) Published
Abstract [en]

Listening to one another is essential to human-human interaction. In fact, we humans spend a substantial part of our day listening to other people, in private as well as in work settings. Attentive listening serves the function to gather information for oneself, but at the same time, it also signals to the speaker that he/she is being heard. To deduce whether our interlocutor is listening to us, we are relying on reading his/her nonverbal cues, very much like how we also use non-verbal cues to signal our attention. Such signaling becomes more complex when we move from dyadic to multi-party interactions. Understanding how humans use nonverbal cues in a multi-party listening context not only increases our understanding of human-human communication but also aids the development of successful human-robot interactions. This paper aims to bring together previous analyses of listener behavior analyses in human-human multi-party interaction and provide novel insights into gaze patterns between the listeners in particular. We are investigating whether the gaze patterns and feedback behavior, as observed in the humanhuman dialogue, are also beneficial for the perception of a robot in multi-party humanrobot interaction. To answer this question, we are implementing an attentive listening system that generates multi-modal listening behavior based on our human-human analysis. We are comparing our system to a baseline system that does not differentiate between different listener types in its behavior generation. We are evaluating it in terms of the participant's perception of the robot, his behavior as well as the perception of third-party observers.

Place, publisher, year, edition, pages
Frontiers Media SA , 2021. Vol. 8, article id 555913
Keywords [en]
multi-party interactions, non-verbal behaviors, eye-gaze patterns, head gestures, human-robot interaction, artificial listener, social signal processing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-299298DOI: 10.3389/frobt.2021.555913ISI: 000673604300001PubMedID: 34277714Scopus ID: 2-s2.0-85110106028OAI: oai:DiVA.org:kth-299298DiVA, id: diva2:1585967
Note

QC 20220301

Available from: 2021-08-18 Created: 2021-08-18 Last updated: 2022-06-25Bibliographically approved

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Jonell, PatrikKontogiorgos, DimosthenisGustafsson, Joakim

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